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Semantics of Subterfuge: Benchmarking Legal Deception Detection Against General-domain State-of-the-Art

Theekshana Samaradiwakara, Nisansa de Silva, George C Lobb
arXiv preprint arXiv:2607.29066

Deception detection has critical implications for legal proceedings, law enforcement, and online security. Although human judgment is limited in accuracy and scalability, Natural Language Processing (NLP) offers a data-driven alternative. We present a survey and comparative analysis of NLP-based Automatic Deception Detection (ADD) focusing on the legal domain, reviewing the evolution from feature-based machine learning to Large Language Model (LLM) approaches. We conduct a unified empirical evaluation across seven datasets (two legal, five general-domain), comparing six fine-tuned transformer models and seven LLMs under four prompting strategies. The results show strong domain sensitivity, with fine-tuned models excelling in data-rich general domains and few-shot LLMs remaining competitive in low-resource legal settings. Chain-of-Thought prompting often underperforms direct classification. These findings highlight the need for domain adaptation and interpretable systems in high-stakes legal contexts.

Keywords: Machine Learning / Deep Learning | Natural Language Processing | Law | Deception Detection | 

@article{samaradiwakara2026semantics,
  title={{Semantics of Subterfuge: Benchmarking Legal Deception Detection Against General-domain State-of-the-Art}},
  author={Samaradiwakara, Theekshana and de Silva, Nisansa and Lobb, George C},
  journal={arXiv preprint arXiv:2607.29066},
  year={2026},
  abstract={Deception detection has critical implications for legal proceedings, law enforcement, and online security. Although human judgment is limited in accuracy and scalability, Natural Language Processing (NLP) offers a data-driven alternative. We present a survey and comparative analysis of NLP-based Automatic Deception Detection (ADD) focusing on the legal domain, reviewing the evolution from feature-based machine learning to Large Language Model (LLM) approaches. We conduct a unified empirical evaluation across seven datasets (two legal, five general-domain), comparing six fine-tuned transformer models and seven LLMs under four prompting strategies. The results show strong domain sensitivity, with fine-tuned models excelling in data-rich general domains and few-shot LLMs remaining competitive in low-resource legal settings. Chain-of-Thought prompting often underperforms direct classification. These findings highlight the need for domain adaptation and interpretable systems in high-stakes legal contexts.},
  misc={https://arxiv.org/pdf/2607.29066,ML:NLP:LAW,https://goo.gl/iY6aTr}
}

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